Training in progress epoch 0

README.md

@@ -3,23 +3,23 @@ library_name: transformers
 license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
-- generated_from_trainer
-metrics:
-- accuracy
+- generated_from_keras_callback
 model-index:
-- name: fine_tuned_model
+- name: rockstar4119/fine_tuned_model
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
 
-# fine_tuned_model
+# rockstar4119/fine_tuned_model
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0005
-- Accuracy: 1.0
+- Train Loss: 0.2876
+- Validation Loss: 0.0311
+- Train Accuracy: 0.9967
+- Epoch: 0
 
 ## Model description
 
@@ -38,28 +38,19 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 5
+- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
+- training_precision: float32
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 151  | 0.0094          | 0.9967   |
-| No log        | 2.0   | 302  | 0.0009          | 1.0      |
-| No log        | 3.0   | 453  | 0.0006          | 1.0      |
-| 0.003         | 4.0   | 604  | 0.0005          | 1.0      |
-| 0.003         | 5.0   | 755  | 0.0005          | 1.0      |
+| Train Loss | Validation Loss | Train Accuracy | Epoch |
+|:----------:|:---------------:|:--------------:|:-----:|
+| 0.2876     | 0.0311          | 0.9967         | 0     |
 
 
 ### Framework versions
 
 - Transformers 4.47.1
-- Pytorch 2.5.1+cu121
+- TensorFlow 2.17.1
 - Datasets 3.2.0
 - Tokenizers 0.21.0

config.json

@@ -26,12 +26,10 @@
   "n_heads": 12,
   "n_layers": 6,
   "pad_token_id": 0,
-  "problem_type": "single_label_classification",
   "qa_dropout": 0.1,
   "seq_classif_dropout": 0.2,
   "sinusoidal_pos_embds": false,
   "tie_weights_": true,
-  "torch_dtype": "float32",
   "transformers_version": "4.47.1",
   "vocab_size": 30522
 }

tf_model.h5

@@ -0,0 +1,3 @@
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+oid sha256:f8510a8954ec2032e5fe04925a60c2023d47292bbe473cce9feb81125db05f1c
+size 267957952